Dealing With Hierarchical Types and Label Noise in Fine-Grained Entity Typing

Fine-Grained entity typing is complicated by the fact that type labels form a hierarchical structure, and those training examples usually contain noisy type labels. This paper addresses these two issues by proposing a novel framework that simultaneously models the correlation among hierarchical type...

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Bibliographic Details
Published inIEEE/ACM transactions on audio, speech, and language processing Vol. 30; pp. 1305 - 1318
Main Authors Wu, Junshuang, Zhang, Richong, Mao, Yongyi, Huai, Jinpeng
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Entity typing
Field effect transistors
fine-grained entity typing
iterative training
label noise detection
Labels
Noise
Noise measurement
Organizations
Predictive models
Structural hierarchy
Task analysis
Training
Training data
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Summary:Fine-Grained entity typing is complicated by the fact that type labels form a hierarchical structure, and those training examples usually contain noisy type labels. This paper addresses these two issues by proposing a novel framework that simultaneously models the correlation among hierarchical types and the noise within the training data. Additionally, the framework contains an innovative training approach during which the noise in the training data is progressively removed. Experiments on standard benchmarking datasets validate the proposed framework and establish it as a new state of the art for this problem.
ISSN:2329-9290
2329-9304
DOI:10.1109/TASLP.2022.3155281